All existing solar cell materials including hybrid perovskites show rather small absorption coefficient (α) of ≈104 cm−1 in the bandgap (Eg) transition region. The weak band‐edge light absorption is an essential problem, limiting conversion efficiency particularly in a tandem solar cell. Herein, all distorted chalcogenide perovskites (BaZrS3, SrZrS3, BaHfS3, and SrHfS3) are found experimentally to exhibit extraordinary high α exceeding 105 cm−1 near Eg, indicating the highest band‐edge α among all known solar cell materials. The giant absorption in the Eg region, which is consistent with the first principles, arises from the intense p–d interband transition enabled by dense S 3p valence states. For solar cell application, low‐gap BaZrS3 derivatives, Ba(Zr,Ti)S3 and BaZr(S,Se)3, are further synthesized. Among the possible candidates of top‐cell materials, an earth‐abundant and nontoxic Ba(Zr,Ti)S3 alloy shows great potential, reaching a maximum potential efficiency exceeding 38% in a chalcogenide perovskite/crystalline Si tandem architecture.

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Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to symbols. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems. Fig. 1. Robot in a home environment that has to deal with complex manipulation, planning, and interaction via semiotic communication with human users.

In this paper, we propose an online algorithm for multimodal categorization based on the autonomously acquired multimodal information and partial words given by human users. For multimodal concept formation, multimodal latent Dirichlet allocation (MLDA) using Gibbs sampling is extended to an online version. We introduce a particle filter, which significantly improve the performance of the online MLDA, to keep tracking good models among various models with different parameters. We also introduce an unsupervised word segmentation method based on hierarchical Pitman-Yor Language Model (HPYLM). Since the HPYLM requires no predefined lexicon, we can make the robot system that learns concepts and words in completely unsupervised manner. The proposed algorithms are implemented on a real robot and tested using real everyday objects to show the validity of the proposed system.